• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.385-392
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• 2016

Market analysis for a business plain is required for the success in the modern world. Most important part in this analysis is pedestrian traffic counting. A traditional way for this is counting it in person. However, it causes high labor costs and mistakes. This paper proposes an automatic algorithm to measure the pedestrian traffic count using images with webcam. The proposed algorithm is composed of two parts: pedestrian area detection and movement tracking. In pedestrian area detection, moving blobs are extracted and pedestrian areas are detected using HoG features and Adaboost algorithm. In movement tracking, multi-level matching and false positive removal are applied to track pedestrian areas and count the pedestrian traffic. Multi-level matching is composed of 3 steps: (1) the similarity calculation between HoG area, (2) the similarity calculation of the estimated position with Kalman filtering, and (3) the similarity calculation of moving blobs in the pedestrian area detection. False positive removal is to remove invalid pedestrian area. To analyze the performance of the proposed algorithm, a comparison is performed with the previous human area detection and tracking algorithm. The proposed algorithm achieves 83.6% accuracy in the pedestrian traffic counting, which is better than the previous algorithm over 11%.

• Journal of the Korean Society for Library and Information Science
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• v.54 no.3
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• pp.21-39
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• 2020

The purpose of this research is to build a mid- and long-term strategic plan for strengthening the status of Gangwon Metropolitan Office of Education as public libraries with the main functions of education, knowledge and culture in the community and helping facilitate the local growth. The current status and needs of Gangwon Metropolitan Office of Education was analyzed with a review of literature and websites, various library statistics, and user surveys and professional focus group interviews conducted during November and December 2019. Based on this analysis, a mission, vision, objectives, strategies, and main tasks of Gangwon Metropolitan Office of Education were established, and 4 strategic goals and 16 major projects were drawn to strengthen the strategic basis of 22 libraries, build specialized collections with a focus on education, recreate user-friendly spaces, and promote services, distinguished from public libraries under the supervision of Gangwon local government. It was necessary for Gangwon Metropolitan Office of Education to enhance and strengthen all of Hardware, Software and Human-ware, equipped with well-organized library building and facilities, collection and services, and professional librarians. In this research, the direction of a mid- and long-term strategic plan was presented for its dynamic operation and sustainable development in the future.

• The Korean Journal of Archival Studies
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• no.42
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• pp.245-285
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• 2014

This study aims to implement a model for developing participatory labor archives for shipbuilding labor archives in young-do, Busan, and to find possibilities of building digital labor archives as participatory ones. The methodology of locality documentation has been applied, and locality archives accepting participation of people with experiences from shipbuilding industry have been examined. Omeka was applied because it is an open-source software and provides additional functions which support various user participations and web-publishing. Following the the model, firstly, a preliminary investigation was conducted and research of participatory agents and records was proceeded. Secondly, it collected and described information of the agents and records by institutions with records and provenance. Thirdly, it developed archival contents specific to events, persons and workplaces in association with archival information. For the follow-up study, plugins were installed and tested to apply for further experiment with participation.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.3117-3126
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• 2018

The operating system update is a basic step to maintain a safe internet use environment. This study analyzed whether the implementation of the operating system update was related to gender and age group during the violation accident prevention act in relation to information protection on the internet, and tried to verify the validity of these factors by analyzing the influence of gender and age group. In this study, logistic regression analysis was conducted based on the information security survey data surveyed by the Korea Internet & Security Agency in 2016. As a result, gender and age were surveyed as factors related to the implementation of operating system updates. As a result of analyzing the impact on the implementation of operating system updates by gender, it is estimated that the odds are 0.419 times higher for women than for men. According to the analysis of the operating system update by age group based on the 50s, which is a vulnerable group of information, the result is that the odds are 13.266 times higher in the 20s than the 50s.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.117-126
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• 2022

Currently, the number of bridge structures in Korea is continuously increasing and enlarged, and the number of old bridges that have been in service for more than 30 years is also steadily increasing. Bridge aging is being treated as a serious social problem not only in Korea but also around the world, and the existing manpower-centered inspection method is revealing its limitations. Recently, various bridge damage detection studies using deep learning-based image processing algorithms have been conducted, but due to the limitations of the bridge damage data set, most of the bridge damage detection studies are mainly limited to one type of crack, which is also based on a close set classification model. As a detection method, when applied to an actual bridge image, a serious misrecognition problem may occur due to input images of an unknown class such as a background or other objects. In this study, five types of bridge damage including crack were defined and a data set was built, trained as a deep learning model, and an open set recognition-based bridge multiple damage recognition model applied with OpenMax algorithm was constructed. And after performing classification and recognition performance evaluation on the open set including untrained images, the results were analyzed.

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.41-48
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• 2023

Recently, there has been a sharp increase in the damages caused by ransomware across various sectors of society, including individuals, businesses, and nations. Ransomware is a malicious software that infiltrates user computer systems, encrypts important files, and demands a ransom in exchange for restoring access to the files. Due to its diverse and sophisticated attack techniques, ransomware is more challenging to detect than other types of malware, and its impact is significant. Therefore, there is a critical need for accurate detection and mitigation methods. To achieve precise ransomware detection, an inference engine of a detection system must possess knowledge of ransomware features. In this paper, we propose a model to extract and classify the characteristics of ransomware for accurate detection of ransomware, calculate the similarity of the extracted characteristics, reduce the dimension of the characteristics, group the reduced characteristics, and classify the characteristics of ransomware into attack tools, inflow paths, installation files, command and control, executable files, acquisition rights, circumvention techniques, collected information, leakage techniques, and state changes of the target system. The classified characteristics were applied to the existing ransomware to prove the validity of the classification, and later, if the inference engine learned using this classification technique is installed in the detection system, most of the newly emerging and variant ransomware can be detected.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.115-126
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• 2017

This study provided digital literacy concept, core area and class achievement connected computational thinking in order to reestablish digital literacy keeping pace with trend change. Main study's contents analyzed digital literacy concept and changing trend of digital technologies, provided characters of information subject matter education. Implications from these analyses is that necessary digital literacy is to support software education, contents being connected computational thinking and contents being necessary. And we committed Delphi investigation to 16 information education expert on total 67 achievements based on these previous studies. Survey results totally surpassed CVR criterion of digital literacy called information life and then readjusting parts of achievements through panel discussions because of partially being lower than Validity criterion. Finally achievements of Information Life came into existence 5 items for 1st, 2nd of elementary school level(ESL), 13 items for 3rd, 4th of ESL, 23 items for 5th, 6th of ESL, 16 items for middle school Level.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.637-643
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• 2014

The purpose of this study was twofold: 1) to analyze the impact factor of clothing and textile-related journals by utilizing the average number of cross-citation that evaluates the relative importance of an academic journal and 2) to provide a list of journals with higher impact factor by analyzing closeness and betweenness among individual journals through graph networks. To fulfill this, a total of 10 clothing and textile-related journals, which are accredited by National Research Foundation of Korea, were analyzed. For analysis of the average number of cross-citation, the targeted research papers were limited to those published between 2008 and 2011 and they were derived from the Korea Citation Index. The software used for network analysis was R ver. 2.15. The results of the study were as follow: First, 'The Korean Society of Knit Design' was indicated as the highest rate of self-citation, followed by 'Journal of the Korean Fashion & Costume Design Association.' Secondly, the average impact power of clothing & textile-related journals was relatively lower (0.681) compared to that (1.00) of 23 journals under the human ecology discipline. 'Korean Journal of Human Ecology' was found to have impact factor of 1.24, which was higher than the average impact factor of human ecology-related journals. Lastly, together with 'Journal of the Korean Society of Clothing and Textiles.'

• Spatial Information Research
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• v.21 no.5
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• pp.43-51
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• 2013

In this study, the concept of spatial big data and effective ways to build a spatial big data system are presented. Big Data is defined as 3V(volume, variety, velocity). Spatial big data is the basis for evolution from 3V's big data to 6V's big data(volume, variety, velocity, value, veracity, visualization). In order to build an effective spatial big data, spatial big data system building should be promoted. In addition, spatial big data system should be performed a national spatial information base, convergence platform, service providers, and providers as a factor of production. The spatial big data system is made up of infrastructure(hardware), technology (software), spatial big data(data), human resources, law etc. The goals for the spatial big data system build are spatial-based policy support, spatial big data platform based industries enable, spatial big data fusion-based composition, spatial active in social issues. Strategies for achieving the objectives are build the government-wide cooperation, new industry creation and activation, and spatial big data platform built, technologies competitiveness of spatial big data.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.603-614
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• 2013

The popularity of mobile devices provides their users with a circumstance that services and information can be accessed wherever and whenever users need. Accordingly, various studies have been proposed personalized methods to improve accessibility of mobile users to information. However, since these personalized methods require users' private information, they gives rise to problems on security. An efficient way to resolve security problems is to estimate user information by using their online and offline behavior. In this paper, for this purpose, it is proposed a novel user information identification system that identifies users' personal and social information by using both his/her behavior on social network services and proximity patterns obtained from GPS data. In the proposed system, personal information of a user like age, gender, and so on is estimated by analyzing SNS texts and POI (Point of Interest) patterns, while social information between a pair of users like family and friend is predicted with proximity patterns between the users. Each identification module is efficiently designed to handle the characteristics of user data like much noise in SNS texts and missing signals in GPS data. In experiments to evaluate the proposed system, our system shows its superiority against ordinary identification methods. This result means that the proposed system can efficiently reflect the characteristics of user data.